Target tracking consists of two main steps: measurement-to-track data association and track filtering. When multiple targets are present in proximity, both steps are more prone to errors. The more the measurement covariances for multiple targets overlap, the greater the data association ambiguity. This chapter presented three sets of techniques for achieving good performance in the face of this ambiguity. One option is to incorporate features into the measurement-to-track cost matrix. This can be a reasonable approach, if the chosen feature is readily and accurately observable and if it segregates the target set. Regardless of the chosen cost function, another approach to resolving measurement-to-track ambiguity is to defer the decision until additional scans of measurements have been collected. The MHT uses this approach, which works well in cases where the ambiguity is likely to resolve overtime. In some cases, the targets may be unresolved or very closely-spaced for long periods of time, necessitating cluster tracking.